code: def test_content(self, pred_keys):
      device = torch.device("cpu:0")
  
      class TestDataset(torch.utils.data.Dataset):
          def __len__(self):
              return 8
  
          def __getitem__(self, index):
              return {
                  "image": torch.tensor([index]),
                  "label": torch.zeros(1),
              }
  
      val_loader = torch.utils.data.DataLoader(TestDataset())
  
      class TestNet(torch.nn.Module):
          def __init__(self, func):
              super().__init__()
              self.func = func
  
          def forward(self, x):
              return self.func(x)
  
      net0 = TestNet(lambda x: x + 1)
      net1 = TestNet(lambda x: x + 2)
      net2 = TestNet(lambda x: x + 3)
      net3 = TestNet(lambda x: x + 4)
      net4 = TestNet(lambda x: x + 5)
  
      class CustomEvents(EventEnum):
          FOO_EVENT = "foo_event"
          BAR_EVENT = "bar_event"
  
      val_engine = EnsembleEvaluator(
          device=device,
          val_data_loader=val_loader,
          networks=[net0, net1, net2, net3, net4],
          pred_keys=pred_keys,
          event_names=["bwd_event", "opt_event", CustomEvents],
          event_to_attr={
              CustomEvents.FOO_EVENT: "foo",
              "opt_event": "opt",
          },
      )
  
      @val_engine.on(Events.ITERATION_COMPLETED)
      def run_transform(engine):
          for i in range(5):
              expected_value = engine.state.iteration + i
              assert_allclose(
                  engine.state.output[0][f"pred_{i}"].item(),
                  expected_value,
              )
  
      @val_engine.on(Events.EPOCH_COMPLETED)
      def trigger_custom_event():
          val_engine.fire_event(CustomEvents.FOO_EVENT)
          val_engine.fire_event(CustomEvents.BAR_EVENT)
          val_engine.fire_event("bwd_event")
          val_engine.fire_event("opt_event")
  
      @val_engine.on(CustomEvents.FOO_EVENT)
      def do_foo_op():
          self.assertEqual(val_engine.state.foo, 0)
  
      @val_engine.on("opt_event")
      def do_bar_op():
          self.assertEqual(val_engine.state.opt, 0)
  
      val_engine.run()
  
file_path: MONAI/tests/test_ensemble_evaluator.py
imports: from __future__ import annotations
  import unittest
  import torch
  from ignite.engine import EventEnum, Events
  from parameterized import parameterized
  from monai.engines import EnsembleEvaluator
  from tests.utils import assert_allclose
label_desc: Use image node_id 1 for calling the TestEnsembleEvaluator obj's underlying member method code with example usage: obj.test_content(pred_keys) and returns: int, dict, self